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Course Detail

Course Name Game Theory
Course Code 19CCE336
Program B. Tech. in Computer and Communication Engineering
Year Taught 2019


Unit 1

Artificial Neural Networks – Pattern classification – Single and Multilayer perceptrons – Backpropagation – Pattern Association – Hebbian learning – Hopfield networks – Bidirectional Associative Memory Networks – Competitive learning – Kohenen’s Self Organizing Maps.

Unit 2

Genetic algorithms – Representation – Reproduction – Crossover and Mutation Operators – Crossover and Mutation rates – Selection mechanisms – Fitness proportionate – ranking and tournament selection – Building Block – Hypothesis and Schema Theorem

Unit 3

Swarm Intelligence – Stigmergy – Competition and Cooperation – Particle Swarm Optimization – Anatomy of a particle – Velocity and Position updation– PSO topologies – Control parameters –Ant Colony Optimization – Pheromone updation and evaporation.


  • Leandro Nunes De Castro, Fernando Jose Von Zuben, “Recent Developments in Biologically Inspired Computing”, Idea Group Publishing, 2005.
  • LaureneFausett, “Fundamentals of neural networks: architectures, algorithms, and applications”, Prentice-Hall, 1994.


  • Goldberg, , ” Genetic algorithms in search optimization and machine learning”, Addison Wesley, 1999.
  • Xin-She Yang, “Recent Advances in Swarm Intelligence and Evolutionary Computation”, Springer International Publishing, Switzerland, 2015.

Evaluation Pattern

Assessment Internal External
Periodical 1 (P1) 15
Periodical 2 (P2) 15
*Continuous Assessment (CA) 20
End Semester 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.

Objectives and Outcomes


  • To introduce the characteristics of natural agents and building blocks involved in biological processes
  • To provide an understanding on the application of bio inspired algorithms to solve complex problems
  • To provide insights into the implementation of bio inspired algorithms

Course Outcomes

  • CO1: To understand phenomena guiding biological processes through self-organization and adaptability
  • CO2: To visualize the effect of low-level interactions on high-level phenomena
  • CO3: To analyze complex engineering problems and solve them by adapting biological processes suitably
  • CO4: To design and implement simple bio-inspired algorithms

CO – PO Mapping

CO1 2 2
CO2 3 2 2
CO3 2 3 2 2 2 2
CO4 2 2 3 3 3 3

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